ANALYSIS AND VISUALIZATION

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• ANALYSIS AND VISUALIZATION
• OF TIME-VARYING DATA
• USING ACTIVITY MODELING
• By
• Salil R. Akerkar
• Advisor
• Dr Bernard P. Zeigler
• ACIMS LAB (University of Arizona)

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Presentation Outline

• Introduction
• Activity A DEVS Concept
• Activity Modeler System
• Stage1 - Preprocessing
• Stage2 - Activity Engine
• Stage3 - Visualization
• Results
• Implications for Discrete Event Simulation
• Future Work

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Introduction

• Data Source and Problem under study
• Current trends
• Unexplored area
• Motivation Discrete Events vs. Discrete Time

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Activity A DEVS Concept

• Definition of Activity

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Activity A DEVS Concept

• Coherency (Space and Time)
• Instantaneous Activity
• Accumulated Activity (same as DEVS Activity)
• Activity Domain

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Activity Modeler System
Stage-1
Stage-2
Stage-3
Raw Data
GNUPLOT MODULES
RESULTS
RESULTS
PERL FORMATTER
ACTIVITY ENGINE
AVS- EXPRESS MODULES
ACTIVITY DATA
FORMATTED DATA
GNUPLOT MODULES
(OPTIONAL) PERL FORMATTER
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Stage 1 Pre Processing

• Why do we need pre-processing?
• Regular Structure format
• PERL formatter
• Functions
• Extract Information
• Format
• Correction Logic
• Analyze part of information
• 2D formatter
• decrease IO operations
• standardization

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Stage 2 Activity Engine
THE ACTIVITY ENGINE
DATA-FILE
PATTERN INFORMATION
PATTERN PREDICTOR
--------------------
ACTIVITY GENERATOR
--------------------
GNUPLOT SCRIPTS
STATISTIC ANALYZER
DATA ENGINE
PERL Formatter
--------------------
--------------------
STATISTICAL INFORMATION
AVS-EXPRESS MODULES
--------------------
ACTIVITY TIME-SERVICES
ACTIVITY LOG
ACTIVITY DATA
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Stage 2 Data Engine

• Functions
• File handling
• Sequential / Random access
• Standardization of filenames for automation
• Memory Allocation
• Transformation between domains
• Cellular and Temporal
• Transformation between dimensions
• Val2Dij Val1DiColsj
• Independent of spatial dimension

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Stage 2 - Activity Generator

• Instantaneous Activity
• Accumulated Activity
• Time Advances
• Activity Factor (AF)
• Cellular domain
• Threshold (AF)

Activity factor
Cells ?
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Stage 2 Statistic Analyzer

• Extract Statistics in terms of groups
• Group1 Maximum, Minimum, Range, Average
• Group2 Standard deviation, Mean
• Group3 Living Factor (Temporal domain)
• Group4 Histogram of Time Advances
• Static in nature
• Provides meaningful threshold to
• Activity Factor
• Living Factor

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Stage 2 Statistic Analyzer

• Group 3 Living Factor (LF)
• Temporal domain
• Group 4 Histogram of Time Advances
• Temporal domain
• Logarithmic in scale

Time ?
Time ?
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Stage 3 Pattern Predictor

• Spatial and Temporal Coherency
• Peaks and Max
• Analyze activity pattern
• Predict activity pattern

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Stage 3 Pattern Predictor

• Max Locator
• Peak Locator
• Difference in Peak and Max

• False Peak problem
• Eliminated by ROI
• (Region of Imminence)

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Stage 3 Region Of Imminence (ROI)

• Definition
• Steps
• Peak Detection in IA
• Scanning algorithm
• Boundary conditions
• Threshold conditions (?)
• Significance
• Imminence Factor

Cells ?
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Stage 3 Pattern Predictor

• 1D scanning algorithm
• 2 neighbors
• Binary visualization

Peak Under consideration 2 Location of cell
10 Initial Values Left-neighbor right-
neighbor 10 Final Values Left-neighbor
7 Right-neighbor 13
1 2 3 4 5 6 7 8 9 10 11 12 13 14
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
31 32 33
Threshold condition
Boundary condition
Cells
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Stage 3 Sphere Of Imminence

• 2D scanning algorithm
• 3 types of tuning
• Coarse
• Normal
• Fine

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Stage 3 Sphere Of Imminence
Fine Tuning
Coarse Tuning
Normal Tuning
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Stage 3 Region Of Imminence
ROI Overcome the False Peak problem
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Stage 3 Predict Pattern

• 1D space
• Linear Span Module
• ? 0.9 0.95
• Order of Pattern
• Pattern attributes
• Offset
• Direction
• Difference
• Steps
• Recognizing pattern tn,n1
• 5 1st order pattern
• 2 2nd order
• Predicting pattern tn2,T

ROI
2nd Order 1st
Linear span
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Stage 3 - Visualization

• Softwares
• GNUPLOT
• AVS-Express
• Visualization Stages
• Reader (Import data)
• Visualization modules
• Writing stage

Reader
VIZ modules
Writer
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Stage 3 - Visualization

• Zero Padding
• Binary Visualization
• Advantages
• Eliminating unwanted data
• Reduction in file size
• Implementation
• set zrange 0.5

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Stage 3 - Visualization
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Results

• 1D space
• 1D heat diffusion process
• Robot Activity
• 2D space
• 2D heat diffusion process
• Fire-Front model

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Results 1D Heat diffusion

• 1D space ,T100
• N10, 100, 200

N 100 10 200
Time?
Cells ?
Cells ?
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Results Robot Activity

• 1D space
• Robots modeled as cells
• Simulation time steps 2357
• Data (Value domain)
• 1- Robot moving
• 0- Robot stopped
• Activity domain
• 1- State transition
• 0- Same state

Robots ?
Time ?
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Results Robot Activity

• Living Factor
• Activity Factor
• Imminent groups

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Results 2D diffusion
Histogram of Time Advances

• 2D space
• (100 x 100 cells)
• T 50
• Cellular domain results (2D)
• Activity Factor
• Statistics
• Surface plot images
• IA surface characterized by
• concentric circles
• tadv histogram lower end

Activity Factor
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Results 2D diffusion
Movie of IA / AA (activity domain) and output
values (value domain)
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Results Fire Front model

• 2D space
• (100 x 100 cells)
• T 297

Movie for Value domain
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Results Fire Front model

• Living Factor
• 20 maximum
• t180 boundary
• Imminence Factor
• ? 0.7
• t 50-150

Time?
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Results Fire Front model
Accumulated Activity
Instantaneous Activity
Peak Bars
Region Of Imminence
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Implications for Discrete Event Simulation

• DEVS transitions
• DTSS transitions
• Maximum Slope
• DEVS v/s DTSS

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Implications for Discrete Event Simulation
DEVS v/s DTSS
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Results Predict Pattern
1D diffusion (N100)
Test data - 3
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Results

• Results for 1D process
• Test data
• 1D diffusion
• Percentage Error decreases as
• N increases
• ROI characterized by linear curves

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Conclusion

• New perspective for data analysis Activity
  domain
• ROI Spatial Coherency in Temporal domain
• Analyze process behavior in terms of Activity
• Compute and Predict activity pattern
• Results process specific
• Predict Pattern - Error decreases as
• N increases
• ROI curves are characterized by linear curves
• DEVS found to be more efficient than DTSS

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Future Work

• Extending system to data in 3D space
• Extending system to UNIX platform
• Enhancing the Pattern predictor module
• Efficiently Detecting the new Imminent Cells in
  DEVS simulation

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ACKNOWLEDGEMENTS

• Dr. Bernard Zeigler
• Dr. Salim Hariri
• Dr. James Nutaro
• Dr. Xiaolin Hu, Alex Muzy
• Hans-Berhard Broeker
• Cristina Siegerist
• ACIMS LAB

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• QUESTIONS ?